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AVirtual Cloud Storage Architecture for Enhanced Data Security

computer Systems science & engineering 2023年第2期44卷 1735-1747页

作者： M.Antony Joans Kumar C.Christopher Columbus E.Ben George T.Ajith Bosco Raj Department of Information Technology St.Xavier’s Catholic College of EngineeringNagercoil629003Tamil NaduIndia Department of Computer Science Engineering PSN College of Engineering and TechnologyTirunelveli627152India Department of Computer Science Engineering University of Technology and Applied SciencesMuscat112Oman Department of Electronics and Communications Engineering PSN College of Engineering and TechnologyTirunelveli627152India

The sensitive data stored in the public cloud by privileged users,such as corporate companies and government agencies are highly vulnerable in the hands of cloud providers and *** proposed Virtual Cloud Storage Archi-tecture is primarily concerned with data integrity and conﬁdentiality,as well as *** provide conﬁdentiality and availability,theﬁle to be stored in cloud storage should be encrypted using an auto-generated key and then encoded into distinct *** the encoded chunks ensured theﬁle integrity,and a newly proposed Circular Shift Chunk Allocation technique was used to determine the order of chunk ***ﬁle could be retrieved by performing the opera-tions in *** the regenerating code,the model could regenerate the missing and corrupted chunks from the *** proposed architecture adds an extra layer of security while maintaining a reasonable response time and sto-rage *** results analysis show that the proposed model has been tested with storage space and response time for storage and *** VCSA model consumes 1.5x(150%)storage *** was found that total storage required for the VCSA model is very low when compared with 2x Replication and completely satisﬁes the CIA *** response time VCSA model was tested with different sizedﬁles starting from 2 to 16 *** response time for storing and retrieving a 2 MBﬁle is 4.96 and 3.77 s respectively,and for a 16 MBﬁle,the response times are 11.06 s for storage and 5.6 s for retrieval.

关键词： Erasure code regenerating code cryptography virtual cloud storage architecture circular shift chunk allocation algorithm

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Enabling Robust Security in MQTT-Based IoT Networks with Dynamic Resource-Aware Key Sharing 4

Enabling Robust Security in MQTT-Based IoT Networks with Dyn...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kaganurmath, Sharadadevi Cholli, Nagaraj Department of Computer Science Engineering Global Academy of Technology Bengaluru India Department of Information Science Engineering RV College of Engineering Bengaluru India

This paper presents a novel approach aimed at developing a secure secret key-sharing system optimized for resource-constrained Internet of Things (IoT) devices. Focusing on the MQTT protocol, the research endeavors to establish secure communication channels between IoT devices and brokers, thereby enhancing the overall security of MQTT-based IoT deployments. This research introduces a novel Dynamic Lightweight Authentication for MQTT (DLA-MQTT) mechanism designed to the unique needs of IoT devices operating under the MQTT protocol. The DLA-MQTT mechanism leverages an innovative lightweight Generalized Feedback Shift Register (GFSR)-based Pseudo-Random Number Generator (PRNG) to generate ephemeral keys, ensuring secure communication while addressing the limitations of computational power and energy resources. Through a detailed comparative analysis with existing cryptographic solutions, the DLA-MQTT mechanism demonstrates superior performance in terms of computational overhead, energy consumption, and execution time, while maintaining robust security against common attacks such as Man-in-the-Middle (MitM) and Denial of Service (DoS). he proposed algorithm's adaptability and scalability are validated using the Cooja simulator, where simulated IoT networks are subjected to various threat scenarios. The results confirm the DLA-MQTT mechanism's efficacy, showcasing a significant reduction in resource utilization without compromising the strength of the security provided. © 2025 The Authors. Published by Elsevier B.V.

关键词： Shift registers

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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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Automated Diabetic Retinopathy detection and classification using stochastic coordinate descent deep learning architectures

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Materials Today: Proceedings 2023年 80卷 3333-3345页

作者： Rosline Mary, A. Kavitha, P. Department of Computer Science and Engineering Vemana Institute of Technology Bengaluru India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India

A new stochastic coordinate descent deep learning architectures optimization is proposed for Automated Diabetic Retinopathy Detection and Classification from different data sets and convolution networks. Initially, the layer-by-layer comparison of convolution matrix, pooling, transition, and dense of each network along with their matrix order is examined. Loss minimization after the testing data from the prediction analysis is considered as objective function for every stage of convolution networks. Similarity among the networks is identified for optimizing the matrix order of each layer and the minimization is performed for classifying the Diabetic Retinopathy level. The performance of the proposed optimization architectures confirmed through confusion matrix for every data set taking training accuracy and data loss as measures. The result of the proposed optimized schemes shows that for different the datasets different in amount data displays the dominance than existing schemes. The numerical analysis of different training accuracy and data loss is presented for validating the proposed work. © 2021

关键词： Convolution

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Transfer Learning Based Facial Emotion Recognition

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SN computer science 2025年第1期6卷 1-13页

作者： Lavanya, M.S. Arun, Vanishri Tapkire, Mayura Suhaas, K.P. Department of Information Science and Engineering JSS Science and Technology University Mysuru India Department of Information Science and Engineering The National Institute of Engineering Mysuru India

The efficiency and accuracy of facial emotion detection imparts on mental health monitoring through Transfer Learning with Facial Emotion Recognition (FER). The Transfer Learning has a resource-efficient and robust solution through FER which has advancing capabilities and FER categorises human emotions through the facial expressions in the images or video sequences. This study explores an advanced FER methodology by utilizing the ResNet-Convolutional Block Attention Module, deep Convolutional Neural Network, known for its depth and enhanced attention mechanisms. The model was pretrained on ImageNet, was fine-tuned to classify facial emotions using the FER + dataset, augmented with preprocessing techniques to enhance robustness and accuracy. A multi-step methodology, starting with data augmentation employed to diversify the training data and overcome overfitting. The study focuses on the ResNet-CBAM architecture, rigorously fine-tuning its hyperparameters and experimenting with the Adam optimizer method. The model achieves a single-network accuracy of 83.74% on FER + dataset. The findings suggest that the model, with appropriate fine-tuning, can serve as a powerful tool for emotion recognition applications. This research contributes to the growing body of knowledge on deep learning for FER, offering insights into model optimization. The proposed method offers a scalable and efficient solution suitable for real-time emotion recognition, with potential applications in affective computing, psychological research. This study demonstrates the effectiveness of ResNet-CBAM in FER, highlighting its potential for various real-world applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adam Convolutional neural network FER ResNet-CBAM T L

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Regenerating Arbitrary Video Sequences with Distillation Path-Finding

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IEEE Transactions on Visualization and computer Graphics 2024年第7期30卷 3622-3635页

作者： Le, Thi-Ngoc-Hanh Yao, Sheng-Yi Wu, Chun-Te Lee, Tong-Yee National Cheng-Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

If the video has long been mentioned as a widespread visualization form, the animation sequence in the video is mentioned as storytelling for people. Producing an animation requires intensive human labor from skilled professional artists to obtain plausible animation in both content and motion direction, incredibly for animations with complex content, multiple moving objects, and dense movement. This article presents an interactive framework to generate new sequences according to the users' preference on the starting frame. The critical contrast of our approach versus prior work and existing commercial applications is that novel sequences with arbitrary starting frame are produced by our system with a consistent degree in both content and motion direction. To achieve this effectively, we first learn the feature correlation on the frameset of the given video through a proposed network called RSFNet. Then, we develop a novel path-finding algorithm, SDPF, which formulates the knowledge of motion directions of the source video to estimate the smooth and plausible sequences. The extensive experiments show that our framework can produce new animations on the cartoon and natural scenes and advance prior works and commercial applications to enable users to obtain more predictable results. © 1995-2012 IEEE.

关键词： Animation

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Personal learning material recommendation system for MOOCs based on the LSTM neural network

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Educational technology and Society 2024年第2期27卷 25-42页

作者： Tzeng, Jian-Wei Huang, Nen-Fu Chen, Yi-Hsien Huang, Ting-Wei Su, Yu-Sheng Department of Information Management National Taichung University of Science and Technology Taiwan Department of Computer Science National Tsing Hua University Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Taiwan

Massive open online courses (MOOCs;online courses delivered over the Internet) enable distance learning without time and place constraints. MOOCs are popular;however, active participation level among students who take MOOCs is generally lower than that among students who take in-person courses. Students who take MOOCs often lack guidance, and the courses often fail to provide personalized learning materials. Artificial intelligence (AI) has been applied to manage increasing amounts of learning data in learners’ online activity records. Driven by the trend in big data, AI technology has drawn increasing attention in various fields. AI-based recommendation systems (RSs) are powerful tools for improving resource acquisition through supply customization, and they can provide personalized learning materials as study guides. In this study, a personalized learning path for MOOCs based on long short-term memory (LSTM) was proposed to meet students’ personal needs for learning. According to students’ video-watching behaviors, we proposed an MOOC material RS that identifies students with similar learning behaviors through clustering and then uses the clustering results and the learning paths of each group of students to construct an LSTM model to recommend learning paths. The system’s learning path recommendations can effectively improve the online participation of learners, and students who received recommendations progressed from the slow-progress group to the medium-progress or fast-progress group. In addition, the learning attitude questionnaire results indicated that the proposed system not only motivated learners to continue learning and achieve high learning capacity but also supported their study planning according to their individual learning needs. © (2023), (International Forum of Educational technology and Society). All Rights Reserved.

关键词： AI-based recommender system Individual learning Knowledge map LSTM MOOCs

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A Comprehensive Analysis of Knowledge Graph-based Social Recommendation System 1

A Comprehensive Analysis of Knowledge Graph-based Social Rec...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Navuluri, Madhavilatha Bhuyan, Hemanta Kumar RGUKT-IIIT Department of Computer Science and Engineering Ongole India Department of Information Technology and Computer Applications Guntur India

ISBN: (纸本)9798331529246

Recommendation systems are crucial due to their high relevance in terms of interpretability and performance. A Social Recommendation system explores how social relations influence user choices and how users select items. In this system, user-embeddings are learned from Knowledge graphs to enhance the recommendations. The social recommendation system can be categorized into three main areas: user recommendation, movie recommendation, and explainable recommendation. User recommendation focusses on providing users with personalized services, movie recommendation enhances the movie-watching experience, and explainable recommendation aims to provide the reasoning behind the suggested items. Data sparsity and the cold start problem are significant challenges faced by RS. The Social RS model addresses these issues by leveraging both user-item interactions and user-user social relationships, thereby improving the overall quality of social network-based recommendations. Methods such as Light Graph Convolutional Network (LGCN), Hyperbolic Recommender (HSR), and Knowledge Enhanced Graph Neural Networks (KEGNN) have been proposed to enhance social recommendation performance. Evaluation metrics such as recall @N, precision @N are commonly used to assess and estimate the effectiveness of recommendation systems. © 2025 IEEE.

关键词： Knowledge graph

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Trajectory-Prediction Techniques for Unmanned Aerial Vehicles (UAVs): A Comprehensive Survey

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1867-1910页

作者： Shukla, Pushpak Shukla, Shailendra Singh, Amit Kumar Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Uttar Pradesh Prayagraj India National Institute of Technology Computer Science and Engineering Department Bihar Patna India

Unmanned Aerial Vehicles (UAVs) have witnessed remarkable significance in diverse sectors, ranging from environmental monitoring, infrastructure inspection, disaster response, wildlife conservation, surveillance, and reconnaissance missions. It is crucial to predict their future states to enable UAVs' safe and efficient operation in dynamic environments. UAV trajectory planning is a crucial aspect of UAV operations, as it determines how the drone will navigate, perform tasks, and avoid obstacles. UAVs can be operated with varying degrees of autonomy, and they can be controlled by humans or autonomously via onboard autopilot software. While existing research has extensively focused on trajectory planning methodologies for UAVs, there is a noticeable gap in the literature concerning the integration of predictive capabilities into trajectory planning, highlighting the need for a comprehensive review of methodologies in UAV trajectory prediction connected with the associated realm of trajectory planning. This article provides a comprehensive and comparative analysis of trajectory prediction methods tailored for autonomous UAVs. Beginning with a precise problem definition and algorithm categorization, our study delves into evaluating methodologies rooted in conventional mathematical models, classical machine learning, deep learning, and reinforcement learning models. © 1998-2012 IEEE.

关键词： Trajectory Autonomous aerial vehicles Data models Predictive models Internet of Things Drones Wireless sensor networks Trajectory planning Safety Optimization

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A Simulation Framework for Prototyping Intelligent Vehicle-to-Infrastructure Applications: A Case Study on RSU-Based Intersection Movement Assist for Connected Autonomous Vehicles

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IEEE Access 2024年 12卷 82584-82598页

作者： Wu, Chun-Ting Wang, Shao-Hua Tu, Chia-Heng National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

A cooperative intelligent transport system (C-ITS) enables information sharing among ITS subsystems, such as vehicle and roadside infrastructure, with vehicle-to-everything (V2X) communications. Novel C-ITS applications aim to reduce traffic congestion and improve road safety. For example, a roadside unit (RSU) can sense the traffic status of an intersection and share the information to nearby vehicles to prevent potential collision, i.e., the intersection movement assist (IMA) scenario. Typically, C-ITS applications are developed in a simulated world to mitigate the high costs and safety hazards associated with the real-world counterpart. Unfortunately, existing simulation tools focus primarily on modeling vehicle-to-vehicle (V2V) communications, rather than the vehicle-to-infrastructure (V2I) communications involved in the above example. This poses a great challenge for developing V2I-based applications, especially for the application-level performance assessment on such systems. This work proposes a software framework that enables the simulation of full software stacks for the ITS subsystems, i.e., vehicle and RSU. This full-stack simulation capability enables hardware-in-the-loop simulations, evaluating the application-level performance and relative cost in the early stage of system development. This can shorten the time to market for C-ITS applications. We believe that this framework paves the way toward the development of novel V2I applications. © 2013 IEEE.

关键词： Intelligent systems

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